# VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain

arXiv: https://arxiv.org/abs/2404.05659 · HTML: https://mlsanity.com/paper/2404.05659/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition](https://mlsanity.com/paper/1805.05225/) (2018)
- [COVID-19 Named Entity Recognition for Vietnamese](https://mlsanity.com/paper/2104.03879/) (2021)
- [Speech recognition for medical conversations](https://mlsanity.com/paper/1711.07274/) (2017)
- [Efficient Utilization of Large Pre-Trained Models for Low Resource ASR](https://mlsanity.com/paper/2210.15445/) (2022)
- [Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project](https://mlsanity.com/paper/2309.15869/) (2023)

## Built on by (most cited first, 9 of 9)

- [GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement](https://mlsanity.com/paper/2406.11546/) (2024)
- [MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder](https://mlsanity.com/paper/2409.14074/) (2024)
- [ViMedCSS: A Vietnamese Medical Code-Switching Speech Dataset & Benchmark](https://mlsanity.com/paper/2602.12911/) (2026)
- [Real-time Speech Summarization for Medical Conversations](https://mlsanity.com/paper/2406.15888/) (2024)
- [Inclusivity of AI Speech in Healthcare: A Decade Look Back](https://mlsanity.com/paper/2505.10596/) (2025)
- [Medical Spoken Named Entity Recognition](https://mlsanity.com/paper/2406.13337/) (2024)
- [wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech](https://mlsanity.com/paper/2408.04174/) (2024)
- [Evaluating Automatic Speech Recognition Systems for Korean Meteorological Experts](https://mlsanity.com/paper/2410.18444/) (2024)
- [SEA-SpeechBench: A Large-Scale Multitask Benchmark for Speech Understanding Across Southeast Asia](https://mlsanity.com/paper/2609.09672/) (2026)

## Abstract (arXiv)

Due to privacy restrictions, there's a shortage of publicly available speech recognition datasets in the medical domain. In this work, we present VietMed - a Vietnamese speech recognition dataset in the medical domain comprising 16h of labeled medical speech, 1000h of unlabeled medical speech and 1200h of unlabeled general-domain speech. To our best knowledge, VietMed is by far the world's largest public medical speech recognition dataset in 7 aspects: total duration, number of speakers, diseases, recording conditions, speaker roles, unique medical terms and accents. VietMed is also by far the largest public Vietnamese speech dataset in terms of total duration. Additionally, we are the first to present a medical ASR dataset covering all ICD-10 disease groups and all accents within a country. Moreover, we release the first public large-scale pre-trained models for Vietnamese ASR, w2v2-Viet and XLSR-53-Viet, along with the first public large-scale fine-tuned models for medical ASR. Even without any medical data in unsupervised pre-training, our best pre-trained model XLSR-53-Viet generalizes very well to the medical domain by outperforming state-of-the-art XLSR-53, from 51.8% to 29.6% WER on test set (a relative reduction of more than 40%). All code, data and models are made publicly available: https://github.com/leduckhai/MultiMed/tree/master/VietMed.